Secure Byzantine-Robust Machine Learning
By Lie He, Sai Praneeth Karimireddy, Martin Jaggi. 2020
68 citations recorded by the source corpus.
Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentralized training raises a number of concerns about the robustness, privacy, and security of the procedure. While extensive work has been done in tackling with robustness, privacy, or security individually, their combination has rarely been studied. In this paper, we propose a secure two-server protocol that offers both input privacy and Byzantine-robustness. In addition, this protocol is communication-efficient, fault-tolerant and enjoys local differential privacy.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Martin Jaggi
- Sai Praneeth Karimireddy
- Lie He
- Ji Liu
- Sebastian U. Stich
- Dejing Dou
- Haoyi Xiong
- Xuhong Li
Citations in the corpus
- From distributed machine learning to federated learning: a survey (2021, methodology): “In order to make the training process robust against the Byzantine failures, the bad users can be identified by analyzing the updated intermediate data using a hidden Markov model [40, 130] or via secure aggregation protocols [70].”
- Learning from History for Byzantine Robust Optimization (2020, background): “Further, combining robustness with notions of privacy and security is also a crucial and challenging problem (He et al., 2020b; So et al., 2020a;b; Jin et al., 2020).”